{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab10026a-1946-47e6-b246-b20c390782b0",
   "metadata": {},
   "source": [
    "# 直方图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93a5949-02ba-4801-a28f-306c10fffbec",
   "metadata": {},
   "source": [
    "直方图是hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec8da98b-145c-4396-ada6-e69ce84612cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d3e9260-224a-4c32-9e14-ea323efbfd59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 均值100\n",
    "mu = 100\n",
    "\n",
    "# 标准差\n",
    "sigma = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f564e1a-d052-450b-853a-2f0e3b6f8a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(loc=mu, scale=15, size=10000)\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# 绘制直方图\n",
    "n, bins, patches = ax.hist(x, 200, density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f4d07e6-a79f-4d65-96ba-327761e16671",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制直方图的同时绘制出概率密度\n",
    "y = (( 1 / (np.sqrt(2*np.pi)*sigma) ) * np.exp(-0.5*(1 / sigma*(bins-mu))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ddbaeb8-ae0f-42d5-9471-091caa2b7e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(loc=mu, scale=15, size=10000)\n",
    "fig, ax = plt.subplots()\n",
    "n, bins, patches = ax.hist(x, 200, density=True)\n",
    "ax.plot(bins, y, '--')\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probility density')\n",
    "plt.title(r'Histogram of IQ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61736581-1ba5-4585-b8c0-78c456ca60eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
